Using Self-Organizing Maps to discover functional relationships of brain areas from fMRI images
| dc.contributor.advisor | Merenyi, Erzsebet | en_US |
| dc.contributor.committeeMember | Kelly, Kevin F. | en_US |
| dc.contributor.committeeMember | Robinson, Jacob T. | en_US |
| dc.contributor.committeeMember | Grossman, Robert | en_US |
| dc.contributor.committeeMember | Karmonik, Christof | en_US |
| dc.creator | O'Driscoll, Patrick | en_US |
| dc.date.accessioned | 2014-10-03T14:52:01Z | en_US |
| dc.date.available | 2014-10-03T14:52:01Z | en_US |
| dc.date.created | 2014-05 | en_US |
| dc.date.issued | 2014-04-23 | en_US |
| dc.date.submitted | May 2014 | en_US |
| dc.date.updated | 2014-10-03T14:52:01Z | en_US |
| dc.description.abstract | This thesis combines a Conscious Self-Organizing Map (SOM) with an interactive clustering method to analyze functional Magnetic Resonance Imaging (fMRI) data to produce improved brain maps compared to maps produced at The Methodist Hospital and in the literature focusing on similar problems. My new maps exhibit an increased level of symmetry, contiguity, coincidence with functional region, and more complete mapping of functional regions. The examined fMRI data contains brain activations of a subject repeatedly executing willed motion in response to a visual stimulus. Clustering the data from this experiment first determines the optimal preprocessing steps for cluster extraction, and second proves that the Conscious SOM provides a valid brain map that identifies interacting brain regions during the sequence of willed motion. I determined that the geometric rectification, motion correction, temporal smoothing, and normalization preprocessing steps facilitate the best clustering. | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.citation | O'Driscoll, Patrick. "Using Self-Organizing Maps to discover functional relationships of brain areas from fMRI images." (2014) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/77381">https://hdl.handle.net/1911/77381</a>. | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/77381 | en_US |
| dc.language.iso | eng | en_US |
| dc.rights | Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder. | en_US |
| dc.subject | SOM | en_US |
| dc.subject | Functional magnetic resonance imaging (fMRI) | en_US |
| dc.subject | Brain map | en_US |
| dc.subject | Self-organizing maps | en_US |
| dc.subject | Clustering | en_US |
| dc.title | Using Self-Organizing Maps to discover functional relationships of brain areas from fMRI images | en_US |
| dc.type | Thesis | en_US |
| dc.type.material | Text | en_US |
| thesis.degree.department | Applied Physics | en_US |
| thesis.degree.discipline | Natural Sciences | en_US |
| thesis.degree.grantor | Rice University | en_US |
| thesis.degree.level | Masters | en_US |
| thesis.degree.name | Master of Science | en_US |